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Item type:Publication, CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2(Center for Open Science, 2022-09-24)Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. Recent works showcase the necessity to improve cloud detection methods for imagery acquired by the Sentinel-2 satellites. However, the lack of consensus and transparency in existing reference datasets hampers the benchmarking of current cloud detection methods. Exploiting the analysis-ready data offered by the Copernicus program, we created CloudSEN12, a new multi-temporal global dataset to foster research in cloud and cloud shadow detection. CloudSEN12 has 49,400 image patches, including (1) Sentinel-2 level-1C and level-2A multi-spectral data, (2) Sentinel-1 synthetic aperture radar data, (3) auxiliary remote sensing products, (4) different hand-crafted annotations to label the presence of thick and thin clouds and cloud shadows, and (5) the results from eight state-of-the-art cloud detection algorithms. At present, CloudSEN12 exceeds all previous efforts in terms of annotation richness, scene variability, geographic distribution, metadata complexity, quality control, and number of samples. The dataset is made publicly available at https://cloudsen12.github.io/. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A distributed N-FINDR cloud computing-based solution for endmembers extraction on large-scale hyperspectral remote sensing data(MDPI, 2022-05-01)In this work, we introduce a novel, distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for endmember extraction, which can be executed on cloud computing environments, allowing users to elastically administer processing power and storage space for adequately handling very large datasets. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, evaluating both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating other endmember extraction algorithms, thus enabling researchers to implement algorithms specifically designed for their own assessment. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Optimization of remotely sensed variables selection for land cover mapping(RELX Group (Netherlands), 2024-01-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Calibration of the SMAP soil moisture retrieval algorithm to reduce bias over the Amazon rainforest(Institute of Electrical and Electronics Engineers Inc., 2024-01-01)Soil moisture (SM) is crucial for the Earth's ecosystem, impacting climate and vegetation health. Obtaining in situ observations of SM is labor-intensive and complex, particularly in remote and densely vegetated regions like the Amazon rainforest. NASA's soil moisture active and passive (SMAP) mission, utilizing an L-band radiometer, aims to monitor global SM. While it has been validated in areas with low vegetation water content (VWC) (< 5 {text{kgm}}{ - 2}), its efficiency in the Amazon, with dense canopies and high VWC (> 10 {text{kgm}}{ - 2}), is limitedly investigated due to scarce in situ measurements. This study assessed and analyzed the SMAP SM retrievals in the Amazon, employing the single-channel algorithm and adjusting vegetation optical depth (τ) and single scattering albedo (ω), two key vegetation parameters. It incorporated in situ SM observations from three old-growth rainforest locations: Tambopata (Southwest Amazon), Manaus (Central Amazon), and Caxiuana (Eastern Amazon). The SMAP SM deviated substantially from the in situ SM. However, calibrating τ and ω values, characterized by a lower τ, resulted in better agreement with the in situ measurements. This study emphasizes the pressing need for innovative methodologies to accurately retrieve SM in high-VWC regions like the Amazon rainforest using SMAP data.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mapping High-Altitude Peatlands to Inform a Landscape Conservation Strategy in the Andes of Northern Peru(Cambridge University Press, 2023-12-12)The wetlands of the jalca ecoregion in the Andes of northern Peru form peat and play a major role in the hydrological ecosystem services of the ecoregion. Although peat is globally valued for carbon sequestration and storage, peatlands have not yet been mapped in the jalca. In this region, the Gocta waterfall, one of the 20 highest waterfalls in the world, depends on the jalca's wetlands ecosystem. The local population depends on tourism to the waterfall and is concerned about preserving its drainage area. To inform conservation planning, in this study we delimited the drainage area of the Gocta waterfall and identified land tenure by applying Geographic Information System (GIS), remote sensing and participatory mapping techniques. Then, by classifying optical, radar and digital elevation models data, we mapped peatland in the jalca of the Gocta drainage area with an overall accuracy of 97.1%. Our results will inform conservation strategy in this complex area of communal, private and informal land tenure systems. At a regional level, this appears to be the first attempt at mapping peatlands using remote sensing imagery in the jalca ecoregion, and it represents a milestone for future efforts to map and conserve peatlands in other tropical mountain areas of the world. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Calibration of the SMAP Soil Moisture Retrieval Algorithm to Reduce Bias over the Amazon Rainforest(European Organization for Nuclear Research, 2023-09-15)Soil moisture (SM) is crucial for the Earth's ecosystem, impacting climate and vegetation health. Obtaining in-situ observations of SM is labor-intensive and complex, particularly in remote and densely vegetated regions like the Amazon rainforest. NASA's Soil Moisture Active and Passive (SMAP) mission, utilizing an L-band radiometer, aims to monitor global SM. While it has been validated in areas with low Vegetation Water Content (VWC) (< 5 kg/m²), its efficiency in the Amazon, which has dense canopies and high VWC (> 10 kg/m²), is uncertain due to scarce in-situ measurements. This study validates and analyzes SMAP data in the Amazon, employing the single-channel algorithm (SCA) and adjusting vegetation optical depth (τ) and single scattering albedo (ω), two key vegetation parameters. It incorporates in-situ SM observations from three old-growth rainforest locations: Tambopata (Southwest Amazon), Manaus (Central Amazon), and Caxiuana (Eastern Amazon). There were substantial discrepancies between SMAP and in-situ data. However, using calibrated τ and ω values, characterized by a lower τ, results in better agreement with in situ measurements. The study emphasizes the pressing need for innovative methodologies to accurately assess SM in high VWC regions like the Amazon using SMAP data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Vanishing of very small glacierets throughout the Northern and Central Andes of Chile(Cambridge University Press, 2026-01-01)Small glaciers ( less than 0.5 km 2 ${ \lt }0.5\,\mathrm{km}^2$ <mml:math xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mnf="http://cambridge.org/core/manifest" xmlns:cup="http://contentservices.cambridge.org" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://cambridge.org/core/metadata" xmlns:core="http://cambridge.org/core" xmlns:c="http://cambridge.org/core/content" display="inline"> <mml:mrow> <mml:mrow> <mml:mo><</mml:mo> </mml:mrow> <mml:mn>0.5</mml:mn> <mml:mspace width="0.167em"/> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">k</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> ), glacierets ( less than 0.25 km 2 ${ \lt }0.25\,\mathrm{km}^2$ <mml:math xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mnf="http://cambridge.org/core/manifest" xmlns:cup="http://contentservices.cambridge.org" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://cambridge.org/core/metadata" xmlns:core="http://cambridge.org/core" xmlns:c="http://cambridge.org/core/content" display="inline"> <mml:mrow> <mml:mrow> <mml:mo><</mml:mo> </mml:mrow> <mml:mn>0.25</mml:mn> <mml:mspace width="0.167em"/> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">k</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> ) and, in particular, very small glacierets ( less than 0.01 km 2 ${ \lt }0.01\,\mathrm{km}^2$ <mml:math xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mnf="http://cambridge.org/core/manifest" xmlns:cup="http://contentservices.cambridge.org" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://cambridge.org/core/metadata" xmlns:core="http://cambridge.org/core" xmlns:c="http://cambridge.org/core/content" display="inline"> <mml:mrow> <mml:mrow> <mml:mo><</mml:mo> </mml:mrow> <mml:mn>0.01</mml:mn> <mml:mspace width="0.167em"/> <mml:msup> <mml:mrow> <mml:mi mathvariant="normal">k</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> </mml:mrow> </mml:math> ), despite being numerous in mountain environments, are underrepresented in scientific inquiry when assessing their response to climate change. We present new insights into the vanishing (no visible surface ice whilst underlain by bedrock or water) of 77 very small glacierets distributed in the Northern and Central Andes of Chile. We also analyse the presumable vanishing (no visible surface ice whilst underlain by regolith) of 244 additional very small glacierets, comprising a total dataset of 321 very small glacierets within the study area, equivalent to the loss of 5.69 times 106 m 3 $5.69\times 10^6\,\mathrm{m}^3$ <mml:math xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mnf="http://cambridge.org/core/manifest" xmlns:cup="http://contentservices.cambridge.org" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://cambridge.org/core/metadata" xmlns:core="http://cambridge.org/core" xmlns:c="http://cambridge.org/core/content" display="inline"> <mml:mrow> <mml:mn>5.69</mml:mn> <mml:mi>×</mml:mi> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>6</mml:mn> </mml:msup> <mml:mspace width="0.167em"/> <mml:msup> <mml:mi>m</mml:mi> <mml:mn>3</mml:mn> </mml:msup> </mml:mrow> </mml:math> of water equivalent ice volume according to the 2022 Chilean Public Glacier Inventory. Our results show that 45.5% of the sample shrank from individually small glaciers at the beginning of the 21st century, whereas 53.0% of the sample vanished after being fragmented from larger glaciers in the same time span. The observed generalised reduction behaviour and vanishing results after extremely dry conditions at the end of the 2009–2022 Central Andes megadrought. We discuss our results in terms of the minimum area threshold for classifying very small glacierets, and whether their vanishing poses a hydrological impact. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, From Lost Trenches to Living Data: Using UAV-Mounted LiDAR to Relocate and Contextualize Legacy Excavations at Saqsaywaman, Cusco, Peru(Wiley, 2026-01-01)LiDAR has transformed archaeological prospection by enabling the detection of sites and landscape features at unprecedented scales, particularly in environments with heavy vegetation. However, its methodological and analytical application at the excavation scale remains underdeveloped. In this paper, we develop a prospection‐oriented workflow that implements UAV‐mounted LiDAR to recontextualize legacy excavations within modern geospatial frameworks. We apply this approach at Saqsaywaman, the monumental Inka acropolis overlooking Cusco, Peru, a UNESCO World Heritage Site with nearly a century of intermittent excavation and uneven documentation. Our LiDAR survey covered approximately 69 ha, producing high‐density point clouds and derived terrain models capable of detecting subtle microtopographic signatures of past excavations obscured by vegetation and erosion. These LiDAR‐derived features were systematically compared with historic maps, archival aerial imagery, excavation reports and oral histories, and were verified through ground‐truthing. The analysis allowed us to relocate, with submeter spatial accuracy, over two dozen undocumented or poorly documented excavation trenches. Beyond preventing redundant excavation, this integration of LiDAR and archival data enabled the recontextualization of legacy stratigraphic data, radiocarbon dates and architectural descriptions alongside new excavations. At Saqsaywaman, the approach clarified preimperial occupation sequences in the Cruz Moqo sector and identified previously unrecognized architectural and hydraulic features. More broadly, the study demonstrates how LiDAR can function as a form of archaeological prospection focused on past research activity, extending the scope of prospection beyond site detection to the recovery and synthesis of legacy excavation data. Many major archaeological sites have been excavated repeatedly over decades, leaving behind fragmented records, imprecise maps and unpublished reports that complicate interpretation and risk redundant or destructive re‐excavation. Our workflow offers a replicable model for cumulative and sustainable archaeological research at sites worldwide. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Spatially Weighted Fidelity and Regularization Terms for Attenuation Imaging(Institute of Electrical and Electronics Engineers Inc., 2025)Quantitative ultrasound (QUS) holds promise in enhancing diagnostic accuracy. For attenuation imaging, the regularized spectral log difference (RSLD) can generate accurate local attenuation maps. However, the performance of the method degrades when significant changes in backscatter amplitude occur. Variations in the technique were introduced involving a weighted approach to backscatter regularization, which, however, is not effective when changes in both attenuation and backscatter are present. This study introduces a novel approach that incorporates an L1-norm for backscatter regularization and spatially varying weights for both fidelity and regularization terms. The weights are calculated from an initial estimation of backscatter changes. Comparative analyses with simulated, phantom, and clinical data were performed. When changes in backscatter and attenuation occur, the proposed approach reduced the lowest root mean square error by up to 73%. It also improved the contrast-to-noise ratio (CNR) by a factor of 4.4 on average compared with previously available methods, considering the simulated and phantom data. In vivo results from healthy livers, thyroid nodules, and a breast tumor further confirm its effectiveness. In the liver, it is shown to be effective at reducing artifacts of attenuation images. In thyroid and breast tumors, the method demonstrated an enhanced CNR and better consistency of the attenuation measurements with the posterior acoustic enhancement. Overall, this approach offers promise for enhancing ultrasound attenuation imaging by helping differentiate tissue characteristics that may indicate pathology.1